Quantum Contagion: A Quantum-Like Approach for the Analysis of Social Contagion Dynamics with Heterogeneous Adoption Thresholds
| Autor: | Ozlem Ozmen Garibay, Ece C. Mutlu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Science QC1-999 heterogeneous adoption thresholds General Physics and Astronomy Emotional contagion Network science 02 engineering and technology Astrophysics 01 natural sciences Information science Article 010305 fluids & plasmas 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Econometrics information diffusion quantum-like social contagion Quantum Physics Probabilistic logic complex networks Complex network technology adoption phase transitions QB460-466 Dynamics (music) 020201 artificial intelligence & image processing Enhanced Data Rates for GSM Evolution |
| Zdroj: | Entropy Volume 23 Issue 5 Entropy, Vol 23, Iss 538, p 538 (2021) |
| ISSN: | 1099-4300 |
| DOI: | 10.3390/e23050538 |
| Popis: | Modeling the information of social contagion processes has recently attracted a substantial amount of interest from researchers due to its wide applicability in network science, multi-agent-systems, information science, and marketing. Unlike in biological spreading, the existence of a reinforcement effect in social contagion necessitates considering the complexity of individuals in the systems. Although many studies acknowledged the heterogeneity of the individuals in their adoption of information, there are no studies that take into account the individuals’ uncertainty during their adoption decision-making. This resulted in less than optimal modeling of social contagion dynamics in the existence of phase transition in the final adoption size versus transmission probability. We employed the Inverse Born Problem (IBP) to represent probabilistic entities as complex probability amplitudes in edge-based compartmental theory, and demonstrated that our novel approach performs better in the prediction of social contagion dynamics through extensive simulations on random regular networks. |
| Databáze: | OpenAIRE |
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